Oil prices and stock returns evidence from vietnamese petroleum and transportation industries

The study aims to examine the impact of oil price changes on the stock returns in Vietnam market by analysing daily data during the period from January 2006 to December 2009.. The intern

Tran Huu Nghi

OIL PRICES AND STOCK RETURNS: EVIDENCE FROM VIETNAMESE PETROLEUM AND TRANSPORTATION

INDUSTRIES

MASTER’S THESIS

HO CHI MINH CITY 2010

Tran Huu Nghi

OIL PRICES AND STOCK RETURNS: EVIDENCE FROM VIETNAMESE PETROLEUM AND TRANSPORTATION

HO CHI MINH CITY 2010

First of all, I would like to express my deepest gratitude and sincere thanks to

my advisor, PhD Nguyen Thu Hien, for her valuable advice, suggestions, and comments throughout every step of my study

Secondly, I would like to thank all my professors for giving me fundamental and academic knowledge during years of my study Especially, I would like to send

my special thanks to Dr Cao Hao Thi, who gave me necessary statistical knowledge to finish this study

Thirdly, my thanks would also go to my classmates, and my colleagues for their supports and encouragements

Finally, I would like to thank my parents, and all members in my family those who are always by my side and encourage me during my study

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The study aims to examine the impact of oil price changes on the stock returns

in Vietnam market by analysing daily data during the period from January 2006

to December 2009 The international multi-factor model is taken as an approach

in the paper in order to investigate the relationship between oil price changes and stock returns of Ho Chi Minh Stock Exchange, petroleum, and transportation industries on Ho Chi Minh Stock Exchange Evidence shows that there are significant links between oil price changes of the previous day and stock returns of these two industries and HoSE These results are useful for investors, managers, and policy makers

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Abstract ii

TABLE OF CONTENTS iii

LIST OF FIGURES v

LIST OF GRAPHS v

LIST OF TABLES vi

ABBREVIATIONS vii

CHAPTER 1: INTRODUCTION 1.1 Rationale of the study 01

1.2 Research questions and objectives 03

1.3 Scope and limitation 03

1.4 Research methodology 03

1.5 Significance of the study 04

1.6 Structure of the study 04

CHAPTER 2: LITERATURE REVIEW 2.1 Theoretical background 06

2.1.1 Market efficiency 06

2.1.2 Capital Asset Pricing Model (CAPM) 08

2.1.3 Arbitrage Pricing Theory (APT) 10

2.1.4 Oil price changes and rationality of Stock market 12

2.2 Previous researches 14

CHAPTER 3: METHODOLOGY AND DATA 3.1 Research methodology 17

3.1.1 Research design 17

3.1.2 Quantitative research 18

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3.2.1 Stock returns 24

3.2.2 Oil price changes 27

3.2.3 Exchange rate returns 28

CHAPTER 4: DATA ANALYSIS 4.1 Desciptive statistics 29

4.2 Regression results 31

4.2.1 Correlations 31

4.2.2 Regression results with OILt-1 33

4.3 Summary of regression results 44

CHAPTER 5: CONCLUSIONS 5.1 Overview 46

5.2 Summary of findings 47

5.3 Limitations and further researches 50

References 52

Appendices Appendix 1: Vietnam petro prices 2006-2009 55

Appendix 2: Regulated amplitude of exchange rate 2006-2009 56

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Figure 1.1: Structure of the study 01

Figure 3.1: Research design 17

Figure 3.2: Research hypotheses 19

Figure 4.1 Hypothesis H1 testing result 36

Figure 4.2 Hypotheses H2, H3, H7 and H8 testing results 41

LIST OF GRAPHS Graph 1.1: Oil prices and VNindex in 2008 01

Graph 3.1: Fluctuation of oil price 2006-2009 23

Graph 3.2: Fluctuation of VNindex 2006-2009 23

Graph 4.1: Histograms and Q-Q plots of daily stock returns 30

Graph 4.2 Histogram and Q-Q plot of index residual 36

Graph 4.3: Histogram and Q-Q plot of petro residual 42

Graph 4.4 Histogram and Q-Q plot of trans residual 42

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Table 3.1: Testing hypotheses for the significance 22

Table 3.2: List of stocks in the petroleum industry 26

Table 3.3: List of stocks in the transportation industry 26

Table 4.1: Descriptive statistics of stock returns, oil price changes, and exchange rate returns 29

Table 4.2: Correlations of Indext, Petrot, Transt with OILt and TWEXt 32

Table 4.3: Correlations of Indext, Petrot, Transt with OILt-1 34

Table 4.4: Regression analysis of the relation between Indext and OILt-1 35

Table 4.5 Hypothesis H1 testing result 36

Table 4.6 Regression analysis of the relation between Ln( 2 Indext ε ) and E(Indext) 38

Table 4.7 Regression analysis of the relation between Petrot and OILt-1 38

Table 4.8 Regression analysis of the relation between Transt and OILt-1 39

Table 4.9 Hypotheses H2, H3, H7 and H8 testing results 40

Table 4.10 Correlation between OILt-1 and Index residual 41

Table 4.11 Corelation between OILt-1 and Index residual 43

Table 4.12 Regression analysis of the relation between Ln( 2 Petrot ε ) and E(Petrot) 44

Table 4.13 Regression analysis of the relation between Ln( 2 Transt ε ) and E(Transt) 45

Table 5.1 Changes in ROE 48

Table 5.2 Summary of the study hypotheses testing results 50

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ADF Augmented Dickey – FullerAPT Arbitrage Pricing TheoryCAPM Capital Asset Pricing ModelGCC Gulf Cooperating Council HoSE Ho Chi Minh Stock ExchangeOLS Ordinary Least Square

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CHAPTER 1: INTRODUCTION

This chapter introduces the rationale of the study, research questions and objectives The study scope, significance, and the structure of the study are also discussed in this chapter

1.1 RATIONALE OF THE STUDY

Oil is a very essential energy for any country all over the world Changes in oil price have become one of the most important factors that contribute to current global economic activity Oil price hikes would make price in commodities increase, and in turn, hearten global inflation and slow down economic growth

Moreover, oil is one of the operational cost factors The higher oil price increases, the higher production cost is For this reason, oil price hikes will cause expected earnings to decline, which bring a decrease in stock prices In other words, increasing oil price potentially affects stock market performance

by altering financial performance or cash flows of companies As illustration

of this, the oil price increased 52.67% between January 2008 and June 2008, and VNindex decreased 52.68% on the same period

Graph 1.1: Oil prices and VNindex in 2008

Many papers in the world investigated the relationship between oil price changes and the stock returns However, the results were various For instance, using an international multifactor model, Basher and Sadorsky (2006) showed a significant relationship between oil price changes and stock markets in emerging countries By contrast, other papers showed that oil price changes do not have significant impacts on stock returns (Chen, Roll, and Ross - 1986, Agusman and Deriantino – 2008) or oil future returns are not correlated with stock market returns, except in the case of oil company returns (Huang et al – 1996) or changes in oil price affect trivially real stock returns in net oil exporting countries (Jones and Kaul – 1996).

Thus, whether there is any relationship between oil price changes and Vietnam stock returns or not is still a big question without answer Therefore, the aim of the study is to examine the impact of oil price changes on Vietnam stock returns in order to find out the answer for that big question The study

is important because of several reasons:

• First, Stock market contribution to the Vietnam economy, which is indicated by the share of market capitalisation to GDP, has been increasing rapidly In 2005, market capitalisation to GDP was 0.69% It went up sharply to 22.6% in 2006, and by the end of 2007 was 40% It is clear that the role of the stock market in the domestic economy is more and more significant, and it is important to examine any risk factors contribute to stock performance, including changes of oil price

• Second, it expects that oil price changes would influence the domestic economy, and this could be caused by their impact on the stock market However, very limited research on this has been made By examining the Vietnam data, the study will contribute to the literature on this area

1.2 RESEARCH QUESTIONS AND OBJECTIVES

The objectives of the study are to examine the impact of oil price changes on Vietnam stock exchange market in general, and to measure the impact of oil price changes on stock returns of petroleum and transportation industries by using daily data during the period from January 2006 to December 2009.The study will be the answer for these questions:

• Is there a relationship between oil price and stock returns in Vietnam?

• How does oil price affect to the Vietnam stock market in general, and to stock returns of petroleum and transportation industry in particular?

1.3 SCOPE AND LIMITATION

Due to time restriction, the study will examine the impact of oil price changes

on Ho Chi Minh Stock Exchange, and measure the impact of oil price changes on stock returns of petroleum and transportation industry on Ho Chi Minh Stock Exchange, which are two on many industries affected directly by oil price changes Data will be collected during the period from January 2006

to December 2009 on Ho Chi Minh Stock Exchange

1.4 RESEARCH METHODOLOGY

This study uses the multiple regression model for the stock returns of Ho Chi Minh Stock Exchange, and petroleum and transportation industries to define the relationship between oil price and Vietnam stock returns, and to measure this relationship

The data for this study consist of daily oil price changes, and stock returns during the period from 2006 to 2009 Daily stock returns are calculated from closing prices of HoSE index, and stocks in petroleum and transportation industries Oil prices are the West Texas Intermediate Spot prices FOB of crude oil, which are available from the Energy Information Administration

1.5 SIGNIFICANCE OF THE STUDY

• Examining the impact of oil price changes on the stock returns helps government to make plans and macroeconomic policies based on predicting of oil price changes so that they could regulate more effectively the Stock Exchange particularly and the domestic economy generally

• Besides, it also gives investors evidences that help them to analyse and to manage their portfolios more efficiently

1.6 STRUCTURE OF THE STUDY

The study consists of 5 chapters:

- Chapter 1 (Introduction) introduces the study This chapter includes the rationale of the study, research objectives, research methodology, significance of the study, and scope and limitation

- Chapter 2 (literature review) shows theoretical background related to the study, and previous researches in the study’s field

- Chapter 3 (Methodology and data) discusses the research methodology, and describes the data used in the study as well

- Chapter 4 (data analysis and findings) analyses data and finds out the impact of oil price changes on stock returns based on the analysed data

- Chapter 5 (conclusions) summarises the study, indicates limitations and suggests possible further researches

Figure 1.1: Structure of the study

Chapter 1: IntroductionChapter 2: Literature reviewChapter 3: Methodology and dataChapter 4: Data analysisChapter 5: Conclusions

CHAPTER 2: LITERATURE REVIEW

This chapter discusses in details the theoretical background related to the study, such as Market Efficiency, Capital Asset Pricing Model, and Arbitrage Pricing Theory Some previous researches are also introduced in this chapter

2.1 THEORETICAL BACKGROUND

2.1.1 Market Efficiency

In finance, there are some models which describe the structure of stock prices based on different factors, such as Capital Asset Pricing Model, and Arbitrage Pricing Theory In fact, investors are much interested in how fast stock prices fluctuate in response to changes to the relevant factors In order to examine these changes, it is very necessary to understand the concept of financial market efficiency

The efficient-market hypothesis was first expressed by Louis Bachelier in his

1900 dissertation, “The Theory of Speculation” Bachelier recognises that “past,

present and even discounted future events are reflected in market price, but often show no apparent relation to price changes” However, his work was

largely ignored until 1950s

The efficient-market hypothesis was developed by Professor Eugene Fama at the University of Chicago Booth School of Business as an academic concept of study through his published Ph.D thesis in the early 1960s at the same school It was generally believed that securities markets were extremely efficient in reflecting information about individual stocks and about the stock market as a whole The accepted view was that when information arises, the news spreads very quickly and is incorporated into the prices of securities without delay Thus, neither technical analysis, which is the study of past stock prices in an

attempt to predict future prices, nor even fundamental analysis, which is the analysis of financial information such as company earnings, asset values, etc., to help investors select “undervalued” stocks, would enable an investor to achieve returns greater than those that could be obtained by holding a randomly selected portfolio of individual stocks with comparable risk.

There are three common forms of financial market efficiency, which were defined in a published paper of Fama in 1970: weak-form efficiency, semi-strong-form efficiency and strong-form efficiency, each of which has different implications for how markets work

In weak-form efficiency, future prices cannot be predicted by analyzing price from the past Excess returns can not be earned in the long run by using investment strategies based on historical share prices or other historical data Technical analysis will not be able to consistently produce excess returns, though some forms of fundamental analysis may still provide excess returns Share prices exhibit no serial dependencies, meaning that there are no "patterns"

to asset prices This implies that future price movements are determined entirely

by information not contained in the price series Hence, prices must follow a random walk

In semi-strong-form efficiency, it is implied that share prices adjust to publicly available new information very rapidly and in an unbiased fashion, such that no excess returns can be earned by trading on that information Semi-strong-form efficiency implies that neither fundamental analysis nor technical analysis will

be able to reliably produce excess returns

In strong-form efficiency, share prices reflect all information, public and private, and no one can earn excess returns If there are legal barriers to private information becoming public, as with insider trading laws, strong-form

efficiency is impossible, except in the case where the laws are universally ignored.

2.1.2 Capital Asset Pricing Model (CAPM)

The Capital Asset Pricing Model (CAPM) is used to specify the relationship between risk and required rates of return on assets when they are held in well-diversified portfolios The model takes into account the asset's sensitivity to non-diversifiable risk (also known as systematic risk or market risk), often represented by the quantity beta (β) in the financial industry, as well as the expected return of the market and the expected return of a theoretical risk-free asset

The model was introduced by Jack Traynor (1961, 1962), William Sharpe (1964), John Lintner (1965a,b) and Jan Mossin (1966) independently, building

on the earlier work of Harry Markowitz on diversification and modern portfolio theory Sharpe, Markowitz and Merton Miller jointly received the Nobel Memorial Prize in Economics for this contribution to the field of financial economics

Basic assumptions of the CAPM1:

1 All investors focus on a single holding period, and they seek to maximize the expected utility of their terminal wealth by choosing among alternative portfolios on the basic of each portfolio’s expected return and standard deviation

2. All investors can borrow or lend an unlimited amount at a given risk-free rate of interest, and there are no restrictions on short sales of any asset

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3 All investors have identical estimates of the expected returns, variances, and covariances among all assets; that is, investors have homogeneous expectations.

4 All investors are price takers (that is, all investors assume that their own buying and selling activity will not affect stock prices)

5. There are no transactions costs or taxes

6 All assets are perfectly divisible and perfectly liquid

7 The quantities of all assets are given and fixed

8 All information is available at the same time to all investors

9 The market is perfect competitive

The formula:

The CAPM is a model for pricing an individual security or a portfolio

The Capital Market Line (CML) specifies the relationship between risk and return for an efficient portfolio that is a combination of the risk-free security and the market-value-weighted portfolio of all risk assets in the economy

CLM: E(R p ) = R f + E(RσM ) - R M f * σ p

where:

⋅E(Rp) is the expected return of the portfolio

⋅E(RM) is the expected return of the market

⋅Rf is the risk-free rate of interest, such as interest arising from government bonds

⋅ σ M is the standard deviation of the market

⋅ σ p is the standard deviation of the portfolio

For individual securities, Security Market Line (SML) is used to show the relationship between risk and return

SML: E(R i ) = R f + [E(R M ) - R f ] * βi

where:

⋅E(Ri) is the expected return of the capital asset

⋅βi (Beta coefficient) is the sensitivity of the expected asset returns to the expected market returns, or also

βi = Covariance between asset i and the market

Variance of market returns =

Cov (Ri, RM)

2

M

σ

⋅[E(RM) - Rf ] is called the market risk premium

2.1.3 Arbitrage Pricing Theory (APT)

In finance, Arbitrage Pricing Theory (APT) is a general theory of asset pricing, that has become influential in the pricing of stock The theory was initiated by the economist Stephen Ross in 1976 Ross argues that if equilibrium prices offer

no arbitrage opportunities over static portfolios of the assets, the expected returns on the assets are approximately linearly related to the factor loadings.Unlike CAPM, a single-factor model, APT can include any number of risk factors, so the required rate of return could be a function of two, three, four, or more factors Thus, the return of risk assets can be expressed as follow:

R i = E(R i ) + [F 1 – E(F 1 )]* βi1 + … + [F j – E(F j )]* βij + εi

where

⋅Ri is the realised rate of return on Stock i

⋅E(Ri) is the expected rate of return on Stock i

⋅Fj is the realised value of economic Factor j

⋅E(Fj)] is the expected value of economic Factor j

⋅βij is the sensitivity of Stock i to economic Factor j

⋅εi is the effect of unique events on the realised return of Stock i (or idiosyncratic risk of Stock i)

Theoretically, an investor could construct a portfolio such that (1) the portfolio was riskless and (2) the net investment in it was zero Such a zero investment portfolio must have a zero expected return, or else arbitrage operations would occur and cause the prices of the underlying asset to change until the portfolio’s

expected return was zero (Eugenne F.Brigham and Michael C Ehrhardt)

Therefore, the APT equivalent of the CAPM’s Security Market Line can be developed as follow:

R i = R f + (R 1 – R f )* βi1 + … + (R j – R f )* βij

Here Rj is the required rate of return on a portfolio that is sensitive only to the jth

economic factor (βj = 1) and has zero sensitivity to all other factors

The primary theoretical advantage of the APT is that it permits several economic factors to influence individual stock returns, whereas the CAPM assumes that the effect of all factors, except those unique to the firm, can be captured in a single measure, the volatility of the stock with respect to the market portfolio Also, the APT requires fewer assumptions than the CAPM and hence is more general Finally, the APT does not assume that all investors hold a market portfolio, a CAPM requirement that clearly is not met in practice

However, the APT faces several major hurdles in implementation, the most severe being that the APT does not identify the relevant factors Thus, APT does not show what factors influence returns, nor does it even indicate how many factors should appear in the model.

Chen, Roll, and Ross (1986) identified the following macro-economic factors as significant in explaining security returns:

⋅Surprises in inflation

⋅Surprises in GNP as indicted by an industrial production index

⋅Surprises in investor confidence due to changes in default premium in corporate bonds

⋅Surprised shifts in the yield curve

As a practical matter, indices or spot or future market prices may be used in place of macro-economic factors, which are reported at low frequency and often with significant estimation errors Market indices are sometimes derived by means of factor analysis More direct indices that might be used are:

⋅Short term interest rates

⋅The difference in long-term and short-term interest rates

⋅Oil prices

⋅Gold or other precious metal prices

⋅Currency exchange rates

2.1.4 Oil price changes and the rationality of the Stock market

In theory, oil price movements have effects on stock valuations The standard cash flow/dividend model shows that the value of stock is equal to the

discounted sum of expected future cash flow Oil, along with capital, labour, and materials represent important components of the production of most goods and services, and changes in the prices of these inputs affects cash flows (Basher, and Sadorsky, 2006) Therefore, a rising in oil prices can make production costs increase, which weakens cash flows and reduces stock prices, and consequently stock returns.

Besides, the discounted rate used in the equity pricing formula is also affected

by increasing in oil prices Rising oil prices are often indicative of inflationary pressures which central bank can control by raising interest rates Higher interest rates make bond look more attractive than stocks leading to a fall in stock prices (Basher, and Sadorsky, 2006)

It is clear that oil price increases negatively or positively impact on stock prices

of companies which are consumers or producers of oil and oil related products Consequently, oil price fluctuations can directly affect stock returns Otherwise, both CAPM and APT show that stock returns are linearly related to risk premiums and other risk factors, such as oil prices, currency exchange rates … Therefore, the relationship between stock returns and oil prices can be expressed

as follow:

R t = α + β*OIL t + εt

where

⋅Rt is stock returns in time t

⋅OILt is oil price changes in time t

⋅εt is a stochastic error term

Moreover, in recent years, many researchers used the International Multi-factor Model as the methodology to analyse the relationship between oil price changes

and stock returns, such as Basher, and Sadorsky (2006), Nandha, and Hammoudeh (2006), Agusman, and Deriantino (2008), and Arouri, and Fouquau (2009) Therefore, the International multi-factor Model is also the approach taken in this study

2.2 PREVIOUS RESEARCHES

There have been many published researches on the relationship between energy prices and stock markets in the world They studied this relationship in many different countries, from developed countries to developing countries and emerging markets

The paper by Chen, Roll, and Ross (1986) is one of the first papers in which investigated the impact of various macroeconomic variables on stock price returns By using the United State monthly data during the period 1958-1984, they found that interest rates, inflation rates, bond yield spreads, and industrial production significantly affect stock returns However, they did not find any evidence that the change in stock price returns is caused by oil price changes

A consistent result was reported by Huang et al (1996) who estimated Vector Auto Regression (VAR) model with daily data over the period 1983-1990 to study the relationship between oil future returns and stock returns in the United

State They argued that “oil future returns are not correlated with stock market

returns, except in the case of oil company returns”.

Jones and Kaul (1996) used quarterly data from 1947-1991 to test whether the reaction of international stock markets (the United State, Japan, the United Kingdom, and Canada) to oil shocks can be justified by changes in real cash flows and/or in expected returns Using the Producer Price Index for Fuels as a measure of oil prices, they did find a relationship between oil prices and stock

returns After including future industrial production into the analysis, the reaction could be accounted by the impact of the oil shock on cash flow for United State and Canada, but the result for Japan and the United Kingdom was inconclusive.

Hammoudeh, and Choi (2005) focused on the relationship among Gulf Cooperating Council (GCC) stock markets and WTI oil spot prices, the United State 3-month Treasury bill rate, and S&P 500 Index Using Vector-error Correction Model, they found that oil price and S&P 500 index have no predictability effect on any GCC market in the short-run

Basher, and Sadorsky (2006) estimated International Multi-factor Model with daily closing prices on 21 emerging stock markets and the Morgan Standley Capital International (MSCI) World Index to study the relationship between oil price risk and emerging stock market returns Data from December 31st, 1992 to October 31st, 2005 showed that oil price risk impacts stock price returns in emerging stock markets

Nandha, and Hammoudeh (2006) investigated the relationship between stock market performance, domestic risk, oil price changes, and foreign exchange in

15 countries in Asia-Pacific Their study was carried out using weekly data over the period May 1994 – June 2004 They found that (a) 13 countries show significant sensitivity to domestic risk, (b) 2 countries (Philippines and South Korea) are oil sensitive to changes in oil price in the short term, and (c) 9 countries are effected by changes in exchange rate

A study which examined the impact of oil price changes on stock returns of nine industry sectors in another Asian country, Indonesia was made by Agusman, and Deriantino (2008) The result of this analysis suggested that oil price changes do not have significant impacts on Indonesian industry stock return However, the

government decision to liberalise domestic oil price in October 2005 has positive impacts on stock returns of the mining sector and negative ones on stock returns of the trading sector.

Hall, and Kenjegaliev (2009) argued that the fluctuations of oil price have an effect on the stock prices when analysing daily data during the period from January 1st, 2000 to March 18th, 2008 of 12 oil companies in USA, Western Europe, Russia, and China The study showed the discrepancy among companies from different economic areas For instance, while oil price change effects stock prices of American and European oil companies, its impact on Chinese and Russian companies is weak

Arouri, and Fouquau (2009) employed linear (Ordinary Least Square Regression) and non-linear investigation to evaluate the relationship between oil price and Gulf Cooperating Council (GCC) countries’ stock market By using weekly data from the first week of June 2005 to the third week of October 2008, the paper found that there are significant links between oil prices and stock returns in Quatar, Oman, and United Arab Emirates In contrast, this relationship cannot be found in Bahrain, Kuwait, and Saudi Arabia

Other researches which studied the impacts of oil price changes on GCC countries’ stock market were also made by Arouri, and Rault (2009) Using two different (weekly and monthly) datasets covering the periods from June 7th, 2005

to October 21st, 2008, and from January 1996 to December 2007, they indicated that oil price increases have a positive impact on stock prices, except Saudi Arabia With the same datasets, they found that the causal relationship is consistently bi-directional for Saudi Arabia in their another study

CHAPTER 3: METHODOLOGY AND DATA

This chapter discusses the research methodology taken by the study, and how

to conduct the quantitative research It also describes how to collect the data used in the study

3.1 RESEARCH METHODOLOGY

3.1.1 Research design:

Study process is illustrated in figure 3.1:

Figure 3.1: Research Design

Data Sources Objectives Literatures

Collect Data

Data Analysis

3.1.2 Quantitative research:

3.1.2.1 Research hypotheses

Basing on the literature review discussed on chapter 2, some hypotheses are suggested to answer research questions and find out the relationship between oil price changes and stock returns in Vietnam market

As mention previously, the impact of rising oil prices on stock returns depends on whether a company is a consumer or producer of oil and oil related products It is easy to realise that oil price changes affect positively petroleum stock returns and negatively transportation ones as the consequence of being producers and consumers of oil and oil related products Besides, since there are more companies in Vietnam stock market that consume oil than produce oil, the overall impact of oil price changes on stock market is expected to be negative

Therefore, the following hypotheses are produced:

H1: VNindex returns are negatively related to oil price changes.

H2: Petroleum stock returns are positively related to oil price changes H3: Transportation stock returns are negatively related to oil price

changes

Moreover, exchange rate volatility is also one factor that affects stock price Oil consumers seem to increase their input costs whereas oil producers get benefits when exchange rate rises

H4: VNindex returns are negatively related to exchange rate returns H5: Petroleum stock returns are positively related to exchange rate

returns

H6: Transportation stock returns are negatively related to exchange rate

returns

In addition, a positive relationship between stock returns and market returns proposes another set of hypotheses

H7: Petroleum stock returns are positively related to VNindex returns H8: Transportation stock returns are positively related to VNindex

returns

Overall, research hypotheses can be described in the figure 3.2

Figure 3.2 Research hypotheses

3.1.2.2 Research models

Following other papers studying the relationship between oil price changes and stock returns, such as Basher and Sadorsky (2006), Nandha and Hammoudeh (2006), Agusman and Deriantino (2008), and Arouri and Fouquau (2009), the study uses the International Multi-factor Model to analyse this relationship in Vietnam This approach is related to the Capital Asset Pricing Model (CAPM), and the Arbitrage Pricing Theory (APT) as discussed in chapter 2 of the study

Transportatio

n stock returns

Exchange rate

Oil price changes

VNindex

Petroleum stock returnsH2

The study estimates the impact of oil price changes on the stock returns of Ho Chi Minh Stock Exchange (HoSE), and two industries (petroleum and transportation industry) on HoSE.

Stock returns of HoSE will be estimated by using Ordinary Least Squares (OLS) from the following multi-factor model (see also Basher and Sadorsky (2006))

Index t = αIndex + β1 OIL t + β2 TWEX t + εIndext (3.1)

where

⋅Indext is the stock return of HoSE in time t

⋅OILt is oil price change in time t

⋅TWEXt is exchange rate return in time t

Exchange rate return, TWEXt, is added as another dependent variable beside oil price changes to examine the impact on stock returns Since the study employs the oil prices in US dollar, exchange rate volatility is one factor that affects stock returns, especially for petroleum and transportation industry whose trading activities involve contracts in US dollar denomination

For petroleum and transportation industry on HoSE, the model estimation follows Arouri and Fouquau (2009)

Petro t = αPetro + β3 OIL t + β4 TWEX t + β5εIndext + εPetrot (3.2) Trans t = αTrans + β6 OIL t + β7 TWEX t + β8εIndext + εTranst (3.3)

In equation (3.2) and (3.3), Petrot and Transt are the stock returns of petroleum and transportation industry in time t; εIndext is the residual of the OLS regression of HoSE returns The market return is included in the models

as a control variable because it is a proxy of changes in aggregate economic wealth which affect risk premium and expected returns of the stocks (Sadorsky, 2001) However, in order to minimise the multi-colinearity, the

model employs the market return after extracting the impacts of oil price changes and exchange rate returns.

The estimation strategy is to use Ordinary Least Squares (OLS) Regression to estimate equation (3.1) for HoSE and equation (3.2), (3.3) for petroleum and transportation industries For each estimation period, the oil betas, and exchange rate betas are estimated and recorded by using EView version 5 This software also helps to describe patterns and general trends in the data set through descriptive summaries like the mean and standard deviation or correlation, or by visualisation of the data through various graphical procedures like histograms, and scatter plots

After that, the study tests regression assumptions in order to prove the appropriateness of models The main assumptions will be tested are:

⋅The dependent variable is normally distributed at each value of the independent variable

⋅The dependent variable is stationary

⋅There is no multicolinearity in the models

⋅The residual is normally distributed

⋅Observations in the residual are independent

⋅There is no presence of heteroskedasticity

Moreover, the study also tests statistical significance of the whole models, and each independent variable Following that, these hypotheses will be tested:

Table 3.1 Testing hypotheses for the significance

(3.3)

H03: β6 = β7 = β8 = 0

H13 at least one of βj s ≠ 0 (j = 6,7,8)

Statistical significance

of each independent

variable

(3.1), (3.2), (3.3)

In 2006, the oil price was rather stable However, it began to go up sharply at the end of 2007 and peaked to US$145.31/barrel in July 2008 The price of crude oil kept on maintaining over US$100/barrel until the end of 2008, and fluctuated around US$70/barrel during 2009

The same as oil price, Vietnam stock return grew up stably in 2006 and 2007 Nevertheless, the market depressed seriously in both Ho Chi Minh Stock Exchange and Ha Noi Stock Exchange in 2008 Ho Chi Minh Stock Exchange went down from more than 1000 points in 2007 to more than 300 points in 2008, and Ha Noi Stock Exchange lost nearby 75% of its value

Graph 3.1: Fluctuation of oil price 2006-2009

Graph 3.2: Fluctuation of VNindex 2006-2009

The same as oil price, Vietnam stock return grew up stably in 2006 and 2007 Nevertheless, the market depressed seriously in both Ho Chi Minh Stock Exchange and Ha Noi Stock Exchange in 2008 Ho Chi Minh Stock Exchange went down from more than 1000 points in 2007 to more than 300 points in 2008, and Ha Noi Stock Exchange lost nearby 75% of its value